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Description
This thesis intends to cover the experimental investigation of the propagation of laser-generated optoacoustic waves in structural materials and how they can be utilized for damage detection. Firstly, a system for scanning a rectangular patch on the sample is designed. This is achieved with the help of xy stages which are connected to the laser head and allow it to move on a plane. Next, a parametric study was designed to determine the optimum testing parameters of the laser. The parameters so selected were then used in a series of tests which helped in discerning how the Ultrasound Waves behave when damage is induced in the sample (in the form of addition of masses). The first test was of increasing the mases in the sample. The second test was a scan of a rectangular area of the sample with and without damage to find the effect of the added masses. Finally, the data collected in such a manner is processed with the help of the Hilbert-Huang transform to determine the time of arrival. The major benefits from this study are the fact that this is a Non-Destructive imaging technique and thus can be used as a new method for detection of defects and is fairly cheap as well.
ContributorsRavi Narayanan, Venkateshwaran (Author) / Liu, Yongming (Thesis advisor) / Zhuang, Houlong (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Droplet-structure interactions play a pivotal role in many engineering applications as droplet-based solutions are evolving. This work explores the physical understanding of these interactions through systematic research leading to improvements in thermal management via dropwise condensation (DWC), and breathable protective wearables against chemical aerosols for better thermoregulation.
In DWC, the heat transfer rate can be further increased by increasing the nucleation and by optimally ‘refreshing’ the surface via droplet shedding. Softening of surfaces favor the former while having an adverse effect on the latter. This optimization problem is addressed by investigating how mechanical properties of a substrate impact relevant droplet-surface interactions and DWC heat transfer rate. The results obtained by combining droplet induced surface deformation with finite element model show that softening of the substrates below a shear modulus of 500 kPa results in a significant reduction in the condensation heat transfer rate.
On the other hand, interactions between droplet and polymer leading to polymer swelling can be used to develop breathable wearables for use in chemically harsh environments. Chemical aerosols are hazardous and conventional protective measures include impermeable barriers which limit the thermoregulation. To solve this, a solution is proposed consisting of a superabsorbent polymer developed to selectively absorb these chemicals and closing the pores in the fabric. Starting from understanding and modeling the droplet induced swelling in elastomers, the extent and topological characteristic of swelling is shown to depend on the relative comparison of the polymer and aerosol geometries. Then, this modeling is extended to a customized polymer, through a simplified characterization paradigm. In that, a new method is proposed to measure the swelling parameters of the polymer-solvent pair and develop a validated model for swelling. Through this study, it is shown that for this polymer, the concentration-dependent diffusion coefficient can be measured through gravimetry and Poroelastic Relaxation Indentation, simplifying the characterization effort. Finally, this model is used to design composite fabric. Specifically, using model results, the SAP geometry, base fabric design, method of composition is optimized, and the effectiveness of the composite fabric highlighted in moderate-to-high concentrations over short durations.
In DWC, the heat transfer rate can be further increased by increasing the nucleation and by optimally ‘refreshing’ the surface via droplet shedding. Softening of surfaces favor the former while having an adverse effect on the latter. This optimization problem is addressed by investigating how mechanical properties of a substrate impact relevant droplet-surface interactions and DWC heat transfer rate. The results obtained by combining droplet induced surface deformation with finite element model show that softening of the substrates below a shear modulus of 500 kPa results in a significant reduction in the condensation heat transfer rate.
On the other hand, interactions between droplet and polymer leading to polymer swelling can be used to develop breathable wearables for use in chemically harsh environments. Chemical aerosols are hazardous and conventional protective measures include impermeable barriers which limit the thermoregulation. To solve this, a solution is proposed consisting of a superabsorbent polymer developed to selectively absorb these chemicals and closing the pores in the fabric. Starting from understanding and modeling the droplet induced swelling in elastomers, the extent and topological characteristic of swelling is shown to depend on the relative comparison of the polymer and aerosol geometries. Then, this modeling is extended to a customized polymer, through a simplified characterization paradigm. In that, a new method is proposed to measure the swelling parameters of the polymer-solvent pair and develop a validated model for swelling. Through this study, it is shown that for this polymer, the concentration-dependent diffusion coefficient can be measured through gravimetry and Poroelastic Relaxation Indentation, simplifying the characterization effort. Finally, this model is used to design composite fabric. Specifically, using model results, the SAP geometry, base fabric design, method of composition is optimized, and the effectiveness of the composite fabric highlighted in moderate-to-high concentrations over short durations.
ContributorsPhadnis, Akshay (Author) / Rykaczewski, Konrad (Thesis advisor) / Wang, Robert (Committee member) / Wang, Liping (Committee member) / Oswald, Jay (Committee member) / Burgin, Timothy (Committee member) / Arizona State University (Publisher)
Created2019
Description
Corrosion fatigue has been of prime concern in railways, aerospace, construction industries and so on. Even in the case of many medical equipment, corrosion fatigue is considered to be a major challenge. The fact that even high strength materials have lower resistance to corrosion fatigue makes it an interesting area for research. The analysis of propagation of fatigue crack growth under environmental interaction and the life prediction is significant to reduce the maintenance costs and assure structural integrity. Without proper investigation of the crack extension under corrosion fatigue, the scenario can lead to catastrophic disasters due to premature failure of a structure. An attempt has been made in this study to predict the corrosion fatigue crack growth with reasonable accuracy. Models that have been developed so far predict the crack propagation for constant amplitude loading (CAL). However, most of the industrial applications encounter random loading. Hence there is a need to develop models based on time scale. An existing time scale model that can predict the fatigue crack growth for constant and variable amplitude loading (VAL) in the Paris region is initially modified to extend the prediction to near threshold and unstable crack growth region. Extensive data collection was carried out to calibrate the model for corrosion fatigue crack growth (CFCG) based on the experimental data. The time scale model is improved to incorporate the effect of corrosive environments such as NaCl and dry hydrogen in the fatigue crack growth (FCG) by investigation of the trend in change of the crack growth. The time scale model gives the advantage of coupling the time phenomenon stress corrosion cracking which is suggested as a future work in this paper.
ContributorsKurian, Bianca (Author) / Liu, Yongming (Thesis advisor) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Alzheimer’s disease (AD) affects over 5 million individuals each year in the United States. Furthermore, most cases of AD are sporadic, making it extremely difficult to model and study in vitro. CRISPR/Cas9 and base editing technologies have been of recent interest because of their ability to create single nucleotide edits at nearly any genomic sequence using a Cas9 protein and a guide RNA (sgRNA). Currently, there is no available phenotype to differentiate edited cells from unedited cells. Past research has employed fluorescent proteins bound to Cas9 proteins to attempt to enrich for edited cells, however, these methods are only reporters of transfection (RoT) and are no indicative of actual base-editing occurring. Thus, this study proposes a transient reporter for editing enrichment (TREE) and Cas9-mediated adenosine TREE (CasMasTREE) which use plasmids to co-transfect with CRISPR/Cas9 technologies to serve as an indicator of base-editing. Specifically, TREE features a blue fluorescent protein (BFP) mutant that, upon a C-T conversion, changes the emission spectrum to a green fluorescent protein (GFP). CasMasTREE features a mCherry and GFP protein separated by a stop codon which can be negated using an A-G conversion. By employing a sgRNA that targets one of the TREE plasmids and at least one genomic site, cells can be sorted for GFP(+) cells. Using these methods, base-edited isogenic hiPSC line generation using TREE (BIG-TREE) was created to generate isogenic hiPSC lines with AD-relevant edits. For example, BIG-TREE demonstrates the capability of converting Apolipoprotein E (APOE), a gene associated with AD-risk development, wildtype (3/3) into another isoform, APOE2/2, to create isogenic hiPSC lines. The capabilities of TREE are vast and can be applied to generate various models of diseases with specific genomic edits.
ContributorsNguyen, Toan Thai Tran (Author) / Brafman, David (Thesis advisor) / Wang, Xiao (Committee member) / Tian, Xiaojun (Committee member) / Arizona State University (Publisher)
Created2020
Description
Shock loading produces a compressive stress pulse with steep gradients in density, temperature, and pressure that are also often modeled as discontinuities. When a material is subject to these dynamic (shock) loading conditions, fracture and deformation patterns due to spall damage can arise. Spallation is a dynamic material failure that is caused by the nucleation, growth, and coalescence of voids, with possible ejection of the surface of the material. Intrinsic defects, such as grain boundaries are the preferred initiation sites of spall damage in high purity materials. The focus of this research is to study the phenomena that cause void nucleation and growth at a particular grain boundary (GB), chosen to maximize spall damage localization.
Bicrystal samples were shock loaded using flyer-plates via light gas gun and direct laser ablation. Stress, pulse duration, and crystal orientation along the shock direction were varied for a fixed boundary misorientation to determine thresholds for void nucleation and coalescence as functions of these parameters. Pressures for gas gun experiments ranged from 2 to 5 GPa, while pressures for laser ablation experiments varied from 17 to 25 GPa. Samples were soft recovered to perform damage characterization using electron backscattering diffraction (EBSD) and Scanning Electron Microscopy (SEM). Results showed a 14% difference in the thresholds for void nucleation and coalescence between samples with different orientations along the shock direction, which were affected by pulse duration and stress level. Fractography on boundaries with strong damage localization showed many small voids, indicating they experience rapid nucleation, causing early coalescence. Composition analysis was also performed to determine the effect of impurities on damage evolution. Results showed that higher levels of impurities led to more damage. ABAQUS/Explicit models were developed to simulate flyer-plate impact and void growth with the same crystal orientations and experimental conditions. Results are able to match the damage seen in each grain of the target experimentally. The Taylor Factor mismatch at the boundary can also be observed in the model with the higher Taylor Factor grain exhibiting more damage.
Bicrystal samples were shock loaded using flyer-plates via light gas gun and direct laser ablation. Stress, pulse duration, and crystal orientation along the shock direction were varied for a fixed boundary misorientation to determine thresholds for void nucleation and coalescence as functions of these parameters. Pressures for gas gun experiments ranged from 2 to 5 GPa, while pressures for laser ablation experiments varied from 17 to 25 GPa. Samples were soft recovered to perform damage characterization using electron backscattering diffraction (EBSD) and Scanning Electron Microscopy (SEM). Results showed a 14% difference in the thresholds for void nucleation and coalescence between samples with different orientations along the shock direction, which were affected by pulse duration and stress level. Fractography on boundaries with strong damage localization showed many small voids, indicating they experience rapid nucleation, causing early coalescence. Composition analysis was also performed to determine the effect of impurities on damage evolution. Results showed that higher levels of impurities led to more damage. ABAQUS/Explicit models were developed to simulate flyer-plate impact and void growth with the same crystal orientations and experimental conditions. Results are able to match the damage seen in each grain of the target experimentally. The Taylor Factor mismatch at the boundary can also be observed in the model with the higher Taylor Factor grain exhibiting more damage.
ContributorsFortin, Elizabeth Victoria (Author) / Peralta, Pedro (Thesis advisor) / Mignolet, Marc (Committee member) / Loomis, Eric (Committee member) / Oswald, Jay (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2020
Description
Phononic crystals are artificially engineered materials that can forbid phonon propagation in a specific frequency range that is referred to as a “phononic band gap.” Phononic crystals that have band gaps in the GHz to THz range can potentially enable sophisticated control over thermal transport with “phononic devices”. Calculations of the phononic band diagram are the standard method of determining if a given phononic crystal structure has a band gap. However, calculating the phononic band diagram is a computationally expensive and time-consuming process that can require sophisticated modeling and coding. In addition to this computational burden, the inverse process of designing a phononic crystal with a specific band gap center frequency and width is a challenging problem that requires extensive trial-and-error work.
In this dissertation, I first present colloidal nanocrystal superlattices as a new class of three-dimensional phononic crystals with periodicity in the sub-20 nm size regime using the plane wave expansion method. These calculations show that colloidal nanocrystal superlattices possess phononic band gaps with center frequencies in the 102 GHz range and widths in the 101 GHz range. Varying the colloidal nanocrystal size and composition provides additional opportunities to fine-tune the phononic band gap. This suggests that colloidal nanocrystal superlattices are a promising platform for the creation of high frequency phononic crystals.
For the next topic, I explore opportunities to use supervised machine learning for expedited discovery of phononic band gap presence, center frequency and width for over 14,000 two-dimensional phononic crystal structures. The best trained model predicts band gap formation, center frequencies and band gap widths, with 94% accuracy and coefficients of determination (R2) values of 0.66 and 0.83, respectively.
Lastly, I expand the above machine learning approach to use machine learning to design a phononic crystal for a given set of phononic band gap properties. The best model could predict elastic modulus of host and inclusion, density of host and inclusion, and diameter-to-lattice constant ratio for target center and width frequencies with coefficients of determinations of 0.94, 0.98, 0.94, 0.71, and 0.94 respectively. The high values coefficients of determination represents great opportunity for phononic crystal design.
In this dissertation, I first present colloidal nanocrystal superlattices as a new class of three-dimensional phononic crystals with periodicity in the sub-20 nm size regime using the plane wave expansion method. These calculations show that colloidal nanocrystal superlattices possess phononic band gaps with center frequencies in the 102 GHz range and widths in the 101 GHz range. Varying the colloidal nanocrystal size and composition provides additional opportunities to fine-tune the phononic band gap. This suggests that colloidal nanocrystal superlattices are a promising platform for the creation of high frequency phononic crystals.
For the next topic, I explore opportunities to use supervised machine learning for expedited discovery of phononic band gap presence, center frequency and width for over 14,000 two-dimensional phononic crystal structures. The best trained model predicts band gap formation, center frequencies and band gap widths, with 94% accuracy and coefficients of determination (R2) values of 0.66 and 0.83, respectively.
Lastly, I expand the above machine learning approach to use machine learning to design a phononic crystal for a given set of phononic band gap properties. The best model could predict elastic modulus of host and inclusion, density of host and inclusion, and diameter-to-lattice constant ratio for target center and width frequencies with coefficients of determinations of 0.94, 0.98, 0.94, 0.71, and 0.94 respectively. The high values coefficients of determination represents great opportunity for phononic crystal design.
ContributorsSadat, Seid Mohamadali (Author) / Wang, Robert Y (Thesis advisor) / Huang, Huei-Ping (Committee member) / Ankit, Kumar (Committee member) / Wang, Liping (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2020
Description
Fusion proteins that specifically interact with biochemical marks on chromosomes represent a new class of synthetic transcriptional regulators that decode cell state information rather than deoxyribose nucleic acid (DNA) sequences. In multicellular organisms, information relevant to cell state, tissue identity, and oncogenesis is often encoded as biochemical modifications of histones, which are bound to DNA in eukaryotic nuclei and regulate gene expression states. In 2011, Haynes et al. showed that a synthetic regulator called the Polycomb chromatin Transcription Factor (PcTF), a fusion protein that binds methylated histones, reactivated an artificially-silenced luciferase reporter gene. These synthetic transcription activators are derived from the polycomb repressive complex (PRC) and associate with the epigenetic silencing mark H3K27me3 to reactivate the expression of silenced genes. It is demonstrated here that the duration of epigenetic silencing does not perturb reactivation via PcTF fusion proteins. After 96 hours PcTF shows the strongest reactivation activity. A variant called Pc2TF, which has roughly double the affinity for H3K27me3 in vitro, reactivated the silenced luciferase gene by at least 2-fold in living cells.
ContributorsVargas, Daniel A. (Author) / Haynes, Karmella (Thesis advisor) / Wang, Xiao (Committee member) / Mills, Jeremy (Committee member) / Arizona State University (Publisher)
Created2019
Description
There are many inconsistencies in the literature regarding how to estimate the Lyapunov Exponent (LyE) for gait. In the last decade, many papers have been published using Lyapunov Exponents to determine differences between young healthy and elderly adults and healthy and frail older adults. However, the differences in methodologies of data collection, input parameters, and algorithms used for the LyE calculation has led to conflicting numerical values for the literature to build upon. Without a unified methodology for calculating the LyE, researchers can only look at the trends found in studies. For instance, LyE is generally lower for young adults compared to elderly adults, but these values cannot be correlated across studies to create a classifier for individuals that are healthy or at-risk of falling. These issues could potentially be solved by standardizing the process of computing the LyE.
This dissertation examined several hurdles that must be overcome to create a standardized method of calculating the LyE for gait data when collected with an accelerometer. In each of the following investigations, both the Rosenstein et al. and Wolf et al. algorithms as well as three normalization methods were applied in order to understand the extent at which these factors affect the LyE. First, the a priori parameters of time delay and embedding dimension which are required for phase space reconstruction were investigated. This study found that the time delay can be standardized to a value of 10 and that an embedding dimension of 5 or 7 should be used for the Rosenstein and Wolf algorithm respectively. Next, the effect of data length on the LyE was examined using 30 to 1300 strides of gait data. This analysis found that comparisons across papers are only possible when similar amounts of data are used but comparing across normalization methods is not recommended. And finally, the reliability and minimum required number of strides for each of the 6 algorithm-normalization method combinations in both young healthy and elderly adults was evaluated. This research found that the Rosenstein algorithm was more reliable and required fewer strides for the calculation of the LyE for an accelerometer.
This dissertation examined several hurdles that must be overcome to create a standardized method of calculating the LyE for gait data when collected with an accelerometer. In each of the following investigations, both the Rosenstein et al. and Wolf et al. algorithms as well as three normalization methods were applied in order to understand the extent at which these factors affect the LyE. First, the a priori parameters of time delay and embedding dimension which are required for phase space reconstruction were investigated. This study found that the time delay can be standardized to a value of 10 and that an embedding dimension of 5 or 7 should be used for the Rosenstein and Wolf algorithm respectively. Next, the effect of data length on the LyE was examined using 30 to 1300 strides of gait data. This analysis found that comparisons across papers are only possible when similar amounts of data are used but comparing across normalization methods is not recommended. And finally, the reliability and minimum required number of strides for each of the 6 algorithm-normalization method combinations in both young healthy and elderly adults was evaluated. This research found that the Rosenstein algorithm was more reliable and required fewer strides for the calculation of the LyE for an accelerometer.
ContributorsSmith, Victoria (Author) / Lockhart, Thurmon E (Thesis advisor) / Spano, Mark L (Committee member) / Honeycutt, Claire F (Committee member) / Lee, Hyunglae (Committee member) / Peterson, Daniel S (Committee member) / Arizona State University (Publisher)
Created2019
Description
This dissertation aimed to evaluate the effectiveness and drawbacks of promising fall prevention strategies in individuals with stroke by rigorously analyzing the biomechanics of laboratory falls and compensatory movements required to prevent a fall. Ankle-foot-orthoses (AFOs) and functional electrical stimulators (FESs) are commonly prescribed to treat foot drop. Despite well-established positive impacts of AFOs and FES devices on balance and gait, AFO and FES users fall at a high rate. In chapter 2 (as a preliminary study), solely mechanical impacts of a semi-rigid AFO on the compensatory stepping response of young healthy individuals following trip-like treadmill perturbations were evaluated. It was found that a semi-rigid AFO on the stepping leg diminished the propulsive impulse of the compensatory step which led to decreased trunk movement control, shorter step length, and reduced center of mass (COM) stability. These results highlight the critical role of plantarflexors in generating an effective compensatory stepping response. In chapter 3, the underlying biomechanical mechanisms leading to high fall risk in long-term AFO and FES users with chronic stroke were studied. It was found that AFO and FES users fall more than Non-users because they have a more impaired lower limb that is not fully addressed by AFO/FES, therefore leading to a more impaired compensatory stepping response characterized by increased inability to generate a compensatory step with paretic leg and decreased trunk movement control. An ideal future AFO that provides dorsiflexion assistance during the swing phase and plantarflexion assistance during the push-off phase of gait is suggested to enhance the compensatory stepping response and reduce more falls. In chapter 4, the effects of a single-session trip-specific training on the compensatory stepping response of individuals with stroke were evaluated. Trunk movement control was improved after a single session of training suggesting that this type of training is a viable option to enhance compensatory stepping response and reduce falls in individuals with stroke. Finally, a future powered AFO with plantarflexion assistance complemented by a trip-specific training program is suggested to enhance the compensatory stepping response and decrease falls in individuals with stroke.
ContributorsNevisipour, Masood (Author) / Honeycutt, Claire (Thesis advisor) / Sugar, Thomas (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Abbas, James (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2019
Description
Electromagnetic fields (EMFs) generated by biologically active neural tissue are critical in the diagnosis and treatment of neurological diseases. Biological EMFs are characterized by electromagnetic properties such as electrical conductivity, permittivity and magnetic susceptibility. The electrical conductivity of active tissue has been shown to serve as a biomarker for the direct detection of neural activity, and the diagnosis, staging and prognosis of disease states such as cancer. Magnetic resonance electrical impedance tomography (MREIT) was developed to map the cross-sectional conductivity distribution of electrically conductive objects using externally applied electrical currents. Simulation and in vitro studies of invertebrate neural tissue complexes demonstrated the correlation of membrane conductivity variations with neural activation levels using the MREIT technique, therefore laying the foundation for functional MREIT (fMREIT) to detect neural activity, and future in vivo fMREIT studies.
The development of fMREIT for the direct detection of neural activity using conductivity contrast in in vivo settings has been the focus of the research work presented here. An in vivo animal model was developed to detect neural activity initiated changes in neuronal membrane conductivities under external electrical current stimulation. Neural activity was induced in somatosensory areas I (SAI) and II (SAII) by applying electrical currents between the second and fourth digits of the rodent forepaw. The in vivo animal model involved the use of forepaw stimulation to evoke somatosensory neural activations along with hippocampal fMREIT imaging currents contemporaneously applied under magnetic field strengths of 7 Tesla. Three distinct types of fMREIT current waveforms were applied as imaging currents under two inhalants – air and carbogen. Active regions in the somatosensory cortex showed significant apparent conductivity changes as variations in fMREIT phase (φ_d and ∇^2 φ_d) signals represented by fMREIT activation maps (F-tests, p <0.05). Consistent changes in the standard deviation of φ_d and ∇^2 φ_d in cortical voxels contralateral to forepaw stimulation were observed across imaging sessions. These preliminary findings show that fMREIT may have the potential to detect conductivity changes correlated with neural activity.
The development of fMREIT for the direct detection of neural activity using conductivity contrast in in vivo settings has been the focus of the research work presented here. An in vivo animal model was developed to detect neural activity initiated changes in neuronal membrane conductivities under external electrical current stimulation. Neural activity was induced in somatosensory areas I (SAI) and II (SAII) by applying electrical currents between the second and fourth digits of the rodent forepaw. The in vivo animal model involved the use of forepaw stimulation to evoke somatosensory neural activations along with hippocampal fMREIT imaging currents contemporaneously applied under magnetic field strengths of 7 Tesla. Three distinct types of fMREIT current waveforms were applied as imaging currents under two inhalants – air and carbogen. Active regions in the somatosensory cortex showed significant apparent conductivity changes as variations in fMREIT phase (φ_d and ∇^2 φ_d) signals represented by fMREIT activation maps (F-tests, p <0.05). Consistent changes in the standard deviation of φ_d and ∇^2 φ_d in cortical voxels contralateral to forepaw stimulation were observed across imaging sessions. These preliminary findings show that fMREIT may have the potential to detect conductivity changes correlated with neural activity.
ContributorsAshok Kumar, Neeta (Author) / Sadleir, Rosalind J (Thesis advisor) / Greger, Bradley (Committee member) / Muthuswamy, Jitendran (Committee member) / Tillery, Stephen H (Committee member) / Sohn, SungMin (Committee member) / Arizona State University (Publisher)
Created2020